Abstract
Dictionary learning has shown its effectiveness in computer vision with the concise expression form but the powerful representation. Dictionary learning represents images with a bag of visual words (BoVW), which is a collection of atoms expressively representative for images. Recently, several task-specific dictionary learning methods have been proposed and successfully applied in medical image analysis, such as de-noising, classification, segmentation, and so on, which promotes the development of computer-aided diagnosis. In this paper, first we give a survey for dictionary learning-based medical image analysis methods including: (1) three discriminative dictionary learning frameworks, (2) CT image de-noising based on dictionary learning, and (3) histopathological image classification using sparse representation. Then, a novel method named Low-rank Shared Dictionary Learning (LRSDL), is presented to achieve accurate glaucoma diagnosis on fundus images. The LRSDL generates a shared codebook for image reconstruction and a particular one to handle the difference between the healthy and glaucomatous images. Benefit from this strategy, LRSDL not only possess distinct glaucoma-related features, but also share common patterns among all the fundus images. Experimental results show that the method effectively delivers glaucoma diagnosis with the accuracy of 92.90%. This endows dictionary learning method a great potential for glaucoma diagnosis and proves the feasibility of its application to medical image analysis.
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Funding
This study was funded by NSFC (Grant Numbers 61702558, 61602527), Hunan Natural Science Foundation (Grant Numbers 2017JJ3411, 2017JJ3416), Primary Research & Developement Plan of Hunan Province (Grant Number 2017WK2074), National Key Research and Development Program of China (Grant Number 2017YFC0840104), China Scholarship Council (Grant Number 201806375006).
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Zhao, R., Li, H. & Liu, X. A Survey of Dictionary Learning in Medical Image Analysis and Its Application for Glaucoma Diagnosis. Arch Computat Methods Eng 28, 463–471 (2021). https://doi.org/10.1007/s11831-019-09383-3
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DOI: https://doi.org/10.1007/s11831-019-09383-3